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Date of publication 10 September 2024; date of current version 29 November 2024.
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DG Comics: Semi-Automatically Authoring
Graph Comics for Dynamic Graphs
Joohee Kim , Hyunwook Lee , Duc M. Nguyen, Minjeong Shin , Bum Chul Kwon ,
Sungahn Ko
, and Niklas Elmqvist
Supporter Top 30%
Main Character
France 0.33 0.33 0.33
Ecuador 0.06 0.06 0.06
El Salvador 0.06 0.06 0.06
Japan 0.32 0.32 0.32
Mexico 0.14 0.14 0.14
Iraq 0.06 0.06 0.06
Finland 0.06 0.06 0.06
Sweden 0.1 0.1 0.1
Yugoslavia 0.19 0.19 0.19
Iran 0.06 0.06 0.06
Australia 0.14 0.14 0.14
1 2
30
Search node
Germany
Poland
Romania
Estonia
Latvia
In August 1939, Germany
and the Soviet Union agreed
to not attack each other and
divide the countries
between them secretly.
Germany, Japan, and Italy
formally became a military alliance,
called the
Axis powers
United States of America
France
Germany
Spain
Switzerland
Romania
Hungary
United States of America
United Kingdom
France
Germany
Switzerland
Canada
Egypt
United States of America
United Kingdom
Italy
Japan
Saudi Arabia
Iraq
Egypt
Syria
Lebanon
Jordan
German Federal Republic
Sankey Node Min. Max Avg Class
Ribbentrop Pact
1941
Level 1.0
Supporter Top 30% Highlighter
Main Character
France 0.33 0.33 0.33
Ecuador 0.06 0.06 0.06
El Salvador 0.06 0.06 0.06
Japan 0.32 0.32 0.32
Mexico 0.14 0.14 0.14
Iraq 0.06 0.06 0.06
Finland 0.06 0.06 0.06
Sweden 0.1 0.1 0.1
Yugoslavia 0.19 0.19 0.19
Iran 0.06 0.06 0.06
Australia 0.14 0.14 0.14
1 2
30
Supporting Character
Australia
Iran
Finland
Yugosla
Brazil
Chile
Bulgaria
Russia
Turkey
Canada
South Africa
Egypt
Venezuela
1 2
France
Search node
Search node
Germany
Russia
Poland
Romania
Estonia
Latvia
Finland
In August 1939, Germany
and the Soviet Union agreed
to not attack each other and
divide the countries
between them secretly.
Meanwhile, Germany, Japan, and Italy
formally became a military alliance, called
theAxis powers
United States of America
France
Germany
Spain
Switzerland
Romania
Hungary
United States of America
United Kingdom
France
Germany
Switzerland
Canada
Egypt
United States of America
United Kingdom
Netherlands
France
Sweden
Switzerland
Czechoslovakia
United States of America
United Kingdom
Italy
Japan
Saudi Arabia
Iraq
Egypt
Syria
Lebanon
Jordan
German Federal Republic
Sankey Node Min. Max Avg Class
Sankey Node
Germany
Poland
United Kingdom
France
Germany
World War II was sparked by the
Nazi German invasion of Poland in 1939.
Britain and France declared war on
Germany on 3 September 1939
.
Phoney
 W
ar
Molotov–
Ribbentrop Pact
 However, in June 1941, it was
terminated due to the German
invasion of the Soviet Union.
Graph Comic Basic Filled Fixed
Timeline ADD REPL
1941
Level 1.0
Supporter Top 30% Highlighter Top 10%
Main Character
France 0.33 0.33 0.33
Ecuador 0.06 0.06 0.06
El Salvador 0.06 0.06 0.06
Japan 0.32 0.32 0.32
Mexico 0.14 0.14 0.14
Iraq 0.06 0.06 0.06
Finland 0.06 0.06 0.06
Sweden 0.1 0.1 0.1
Yugoslavia 0.19 0.19 0.19
Iran 0.06 0.06 0.06
Australia 0.14 0.14 0.14
1 2
30
Supporting Character
EDIT
Australia 38.56
Iran 38.2
Finland 37.99
Yugoslavia 23.55
Brazil 23.52
Chile 20.27
Bulgaria 19.04
Russia 15.67
Turkey 10.86
Canada 9.67
South Africa 8.81
Egypt 7.83
Venezuela 6.31
1 2
30
France
Summary Node Attribute Community
GENERATE
6
Search node
Search node
Germany
Soviet Union
Poland
Romania
Estonia
Latvia
Finland
In August 1939, Germany
and the Soviet Union agreed
to not attack each other and
divide the countries
between them secretly.
Meanwhile, Germany, Japan, and Italy
formally became a military alliance, called
the
Axis powers
United States of America
France
Germany
Spain
Switzerland
Romania
Hungary
United States of America
United Kingdom
France
Germany
Switzerland
Canada
Egypt
United States of America
United Kingdom
Netherlands
France
Sweden
Switzerland
Czechoslovakia
United States of America
United Kingdom
Italy
Japan
Saudi Arabia
Iraq
Egypt
Syria
Lebanon
Jordan
German Federal Republic
Sankey Node Min. Max Avg Class
Sankey Node Weight
Germany
Poland
United Kingdom
France
Germany
World War II was sparked by the
Nazi German invasion of Poland in 1939.
Britain and France declared war on
Germany on 3 September 1939.
Phoney
 War
Molotov–
Ribbentrop Pact
 However, in June 1941, it was
terminated due to the German
invasion of the Soviet Union.
United States of America
United Kingdom
France
Germany
Sweden
Poland
Austria
Czechoslovakia
France
Italy
China
Japan
Hawaii
Yugoslavia
Singapore
Ethiopia
Cambodia
Hungary
Solomon Islands
Hong Kong
British Somaliland
Greece
Romania
Bulga
ria
United States of America
United Kingdom
Netherlands
France
Sweden
Switzerland
Czechoslovakia
United States of America
United Kingdom
Italy
Japan
Saudi Arabia
Iraq
Egypt
Syria
Lebanon
Jordan
German Federal Republic
font
BG
1931
1932
1933
1934
United Kingdom
China
United Kingdom
China
United Kingdom
China
The Big Three
—the United Kindom, the United
States and the Soviet Union
—, China and France
officially formed
the United Nations to oppose
Italy
China
Hawaii
Yugoslavia
Singapore
Ethiopia
Cambodia
Solomon Islands
Hong Kong
British Somaliland
Bulgaria
Germany
Soviet Union
 However, i
n June 1941, it was
terminated due to the German
invasion of the Soviet Union.
The Allies
The Axis powers rapidly expanded their influence in
Europe, Africa, and Asia.
The attack on Pearl Harbor led
the U.S. to formally enter WWII
24. 4. 1. 오후 4:07
Graph Comic
Graph Comic
Timeline ADD
United States of America
United Kingdom
France
Soviet Union
China
Germany
X
Battle of Midway
Battle of Stalingrad
On May 8, 1945, World War II in Europe ended.
Japan surrendered to the Allies on September 2.
The End of
World War II
Japan
Fig. 1: World War II. Graph comic on the history of World War II, covering the causes and significant events, such as the formation
of alliances and major battles, created using DG Comics. Note that while the under lying dynamic graph comic was generated
automatically, the DG Comics tool provides functionality for the designer to manually move nodes, add visual highlighting, and insert
the geographic map backgrounds. We used contemporary national flags for easier recognition wherever feasible.
Abstract—Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs—graphs whose
vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone.
In this paper, we propose DG COMICS, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build
and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic
graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from
dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we
provide an example and report the results of a user study and an expert review.
Index Terms—Data-driven storytelling, narrative visualization, dynamic graphs, graph comics
1 INTRODUCTION
Dynamic systems are prevalent in both nature and society. Catalysts
facilitate chemical reactions, while species interact throughout evolu-
Corresponding author
Joohee Kim, Hyunwook Lee, Duc M. Nguyen, and Sungahn Ko are with
UNIST (Ulsan National Institute of Science and Technology).
E-mail: {joohee, gusdnr0916, ducnm, sako}@unist.ac.kr
Bum Chul Kwon is with IBM Research. E-mail: bumchul.kwon@us.ibm.com
Niklas Elmqvist is with Aarhus University. E-mail: elm@cs.au.dk
tion. Scientists collaborate with colleagues and students across various
stages of their careers. Social relationships form, evolve, and dissolve
as individuals make friends, have children, and experience rifts or pass
away. Modeling these phenomena as dynamic graphs, where nodes rep-
resent entities and links represent their evolving relationships over time,
is a valuable tool for understanding such systems. However, due to
inherent complexity and scale, it is challenging to communicate stories
extracted from dynamic graphs succinctly and accurately. In previous
studies, graph comics, a comic-based storytelling medium that consists
of graph visualizations, is found to be effective in narrating stories
involving dynamic graphs [4, 65,86]. Despite the potential benefits, it
is time-consuming for users to create such graph comics based on their
data, especially given the lack of dedicated authoring tools.
We address this gap by proposing an approach to automatically
generate graph comics from hierarchical clustering that preserves the
temporal causality (thus causality-preserving) of the events. To validate
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974
the approach, we implement an interactive tool called DG COMICS:
T1
Storytelling Automation: To combat the complexity of dynamic
graphs with many relationships spanning a long period of time,
DG Comics automates the clustering of temporal events into seg-
ments using causality-preserving hierarchical aggregation [24].
T2
Storytelling Agency: To facilitate the creative and functional
agency of the analyst authoring the story, DG Comics yields con-
trol of the aggregation level, the main and supporting characters
to display, as well as style and formatting choices to the analyst.
DG Comics offers a causality-preserving clustering of graph snap-
shots in a dendrogram, which users can leverage to initiate graph comic
generation. From a chosen aggregation level, which determines the
number of panels, DG Comics automatically creates a comic template
with a computed layout. Each panel features the main character(s)
undergoing the most significant changes and their relationships. These
changes are depicted with distinct visual representations and captions
generated using a template-based approach. Users can further develop
the template using various editing functions and build original stories
through interaction with additional views and tables. We verified DG
Comics’s usefulness and versatility through (1) an example using a
VIS coauthorship network, (2) a user study involving 13 university
participants designing graph comics with an international trade network
dataset, and (3) interviews with six experts from various domains.
The contributions of this work include (1) robust design requirements
derived from challenges identified in prior research; (2) development of
a causality-preserving clustering technique with enhanced graph simi-
larity computation as the backbone of automation; (3) implementation
of a novel interactive comic authoring tool; and (4) results from an
example scenario, a user study, and expert reviews.
2R
ELATED WORK
Our work lies at the intersection of graph visualization [75], data-driven
storytelling [59], and data comics [86]. Below we review the literature
in all these areas and discuss how our work supersedes prior art.
2.1 Graph Visualization
A graph or a network consists of nodes and links, with nodes rep-
resenting entities (e.g., people, organizations) and links representing
relationships (e.g., friendships, alliances). Graphs are widely used in
domains [75] such as transportation, biology, communication, business,
and security. As their utility increases, so does their size. Combin-
ing networks with data like maps (geospatial graphs), time (dynamic
graphs) [25], or text adds complexity. Conventional graph visualiza-
tions include two methods: node-link diagrams [28], which use lines
to connect nodes and visual channels (e.g., color, size, line thickness)
to distinguish attributes, and adjacency matrices [12, 30, 31], which
organize nodes in a grid and display connections at intersecting cells.
Prior art [51, 55, 75] provides detailed descriptions.
As graphs grow, visualizations become complex and cluttered. Net-
workNarratives [46] provides semi-automated guided data tours to
facilitate the navigation of complex networks. Symbolic representa-
tions are one way to overcome this issue by aggregating or hiding nodes.
Dunne and Shneiderman [23] propose a simplification technique that
uses fan, connector, and clique motifs to save space and improve un-
derstanding of large graphs. To prevent misunderstanding and address
the complexity of large graphs, some research [30, 31] combines node-
link diagrams and adjacency matrices, enabling efficient exploration
of networks. Yoghourdjian et al. [83] introduce graph thumbnails for
high-level structure visualization, allowing easy identification, com-
parison, and overview of multiple large graphs. Ghani et al. [28] use
dynamic insets to show off-screen node neighborhoods, while May
et al. [49] improve off-screen awareness by providing graph neighbor
information. Visualizing groups or clusters in graphs is another area
of research. Saket et al. [63] propose a taxonomy for graph groups,
enumerating tasks such as group-only, group-node, group-link, and
group-network. Vehlow et al. [74] survey techniques for visually pre-
senting graph groups, categorizing them into visual node attributes,
juxtaposed, superimposed, and embedded methods.
2.2 Visualizing Dynamic Graphs
Dynamic graphs are graphs whose relations among entities change over
time. Such changes bring challenges in tasks, visualization, and evalua-
tion, prompting significant research efforts. Ahn et al. [1] categorize
temporal features of dynamic graphs by the rate and shape of changes
and individual events. Beck et al. [11] survey dynamic graph visualiza-
tion techniques, focusing on presentation methods such as animated di-
agrams and static timeline-based charts. Analyses of temporal features
from individual node/link, group, and network perspectives inspired
how DG Comics presents information on dynamic graphs. Numerous
techniques have been developed for visualizing dynamic graphs. Elzen
et al. [71] visualize graph evolution with sequence views, and Burch
et al. [16] propose pixel-oriented visualizations. GeneaQuilts [13] use
a hybrid adjacency matrix and node-link representation to visualize
family trees over time. Bach et al. [3, 5] employ 3D matrix cubes and
small multiples of adjacency matrices.
To reduce the complexity of dynamic graphs, computational methods
like spectral graph wavelets [22] and diachronic node embedding [81]
hierarchically aggregate [24] graph snapshots based on graph struc-
tures [2] or attributes [29]. The computation results effectively visualize
the changes by time (e.g., small multiples [3]). For example, Elzen et
al. [72] present a novel visual analytics pipeline that allows users to
track graph changes with points. The pipeline consists of democrati-
zation, vectorization and normalization, dimensional reduction, and
visualization, transforming graph snapshots into points. Cakmak et
al. [18] propose multi-scale snapshot visualization with graph2vec [53],
creating temporal summaries of dynamics graphs. We refer to [80] for
an extensive survey result on graph learning algorithms.
2.3 Graph Comics and Authoring Tools
Comics [50] are a storytelling genre that presents stories with combina-
tions of illustrations, text, and annotations on various layouts.Segel and
Heer propose using comics for data-driven storytelling in their seminal
work on narrative visualization [65]. Data comics, an emerging medium
of data-driven narrative visualization, leverage the visual language of
comics, including layouts, characters, and captions [6,86]. Research has
focused on identifying characteristics of data comics [70], developing
authoring tools [20, 86], and specific applications such as visualization
education [76] and user study reports [77]. Bach et al. [4] conduct a
design study on storytelling with dynamic networks, proposing design
factors: visual representation of graph elements and changes, tempo-
rality of changes, element identity, cast of characters, level of detail,
overview and detail, and representation of multivariate networks. They
also define a design space for data comics by analyzing common pat-
terns in existing storytelling media (e.g., infographics, data videos) [7].
This design space has two dimensions: content relation patterns (e.g.,
narrative, temporal, faceting, visual encoding, granular, spatial patterns)
and panel layout (e.g., linear, tiled, parallel, grid). Wang et al. [79] con-
duct controlled and in-the-wild user studies to investigate the benefits
of data comics compared to infographics. Their experiments reveal that
data comics improve understanding and recall of information, and are
preferred for enjoyment, focus, and engagement.
Several data comics authoring tools have been proposed for various
computing environments (e.g., tablets, computational notebooks, cod-
ing environments). DataToon [40] is the first effort to help users design
comics on dynamic graphs with pen and touch interaction. Computa-
tional notebooks are a new tool for analyzing, visualizing, and sharing
datasets. However, their results, which combine programming code,
notes, and analysis, often struggle to communicate with the audience.
To address this, Kang et al. [38] propose ToonNote, an extension that
converts notebooks into data comics. CodeToon [69], similar to Toon-
Note in motivation, focuses more on coding environments, facilitating
code-aligned storytelling and automated comic generation. Most data
comics are static to guide readers through a specific flow and layout.
Wang et al. [78], posing questions on the interactivity of data comics,
formalize operations for interactive comics (e.g., content highlighting,
panel addition/removal). They also present a lightweight scripting
approach with six goals: navigation, details on demand, changing
perspective, branching, pause and reveal, and input data.
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975
kim ET AL.: DG COmiCS: SEmi-AUTOmATiCALLY AUTHORiNG GRAPH COmiCS FOR DYNAmiC GRAPHS
2.4 Comparison to Prior Art
Our proposed work in this paper uses comics to visualize dynamic
graphs changing over time, and our approach is novel over the literature:
Building on Segel and Heer [65]’s taxonomy, Zhao et al. [86] first
proposed data comics for narrative visualization, but our approach
goes beyond their work by applying the idea to graphs.
While Bach et al. [4] apply comics to graphs, our work proposes
automatic graph comic generation as well as an interactive editor.
Authoring tools for data comics exist [38, 40, 69, 86], but ours is
unique to dynamic graphs and semi-automatically builds comics.
Prior work visualizes temporal summaries of dynamic graphs [3,
18, 85], but we apply the idea to automating data comics.
3C
HALLENGES AND DESIGN REQUIREMENTS
The aim of this paper is to develop an authoring tool for data comics that
automatically generates a narrative in comic form and enables users
to efficiently reorganize them into a coherent storyline. We accom-
plish this by adhering to the two design principles outlined in Sec. 1:
storytelling automation versus storytelling agency. These principles
guide authors in managing scale and complexity while preserving their
creative and expressive vision for the data-driven story.
To achieve these goals, we reviewed existing fundamental design
principles formalized for storytelling [7, 33, 42, 45, 65, 68] and data
comics [4, 6, 7, 84, 86]. We also based our work on prior research in
dynamic graphs regarding visual analysis [37, 51, 74, 75]), tasks [1, 11,
25], and computational methods [22, 72, 81], as well as data comics
authoring tools [38, 40, 69, 78]. Based on the design principles and
literature survey, we derive several challenges, as listed below.
C1–Size and Complexity. Creating comics from dynamic graph
data demands identifying key narrative elements, such as pivotal nodes
or events, for storytelling. This task ranges from emphasizing criti-
cal nodes to elaborating on connections for specific events. Viewing
dynamic graphs through various lenses, like individual nodes or node
communities, can reveal these elements. However, analyzing dynamic
graphs across multiple perspectives and timeframes is challenging due
to the size and complexity of general dynamic networks.
C2–Identifying Characters. Because data comics are a genre
of comics, they follow common design patterns in comics [50]. An
important pattern is the existence of main and supporting charac-
ters [6, 42, 65, 70, 86]. Main characters drive the story while supporting
characters play key roles in the narrative. Identifying which nodes serve
as main or supporting characters poses a challenge, given the multitude
of nodes with varying and evolving characteristics.
C3–Changes Over Time. The single most important task in a dy-
namic graph visualization is to show changes in the graph over time [1],
particularly for the main and supporting characters, or their relation-
ships. The challenge lies in detecting the changes, presenting them,
and annotating the story. Detecting temporal changes requires a deep
understanding of the data, and is difficult to do manually, especially for
large graphs. Visualizing such changes is also not a trivial problem. Fi-
nally, annotations are essential for explaining the context or conveying
additional insights.
C4–The Language of Comics. Comics employ a distinctive
narrative structure, visual language, and temporal sequencing to engage
readers. For end-users unfamiliar with these conventions, effectively
integrating them into dynamic graph comics can be daunting. Ensuring
these elements are used effectively to convey complex information in
an accessible and compelling manner requires a deep understanding of
comic artistry and narrative technique.
Design Requirements. Below we present design requirements
(R1–R5) for our proposed data comics authoring tool that can help
users overcome these challenges (C1–C4):
R1
Allow users to choose temporal granularities for the data comics
that reflect their desired levels of detail (C1).
R2
Enable multi-perspective storytelling (individual, community-
based, metric-based, etc) (C1).
R3
Help users find main characters and supporters for initiating a
narrative (C2).
R4
Support users to detect and present temporal changes of dy-
namic graphs with appropriate annotations (C3).
R5
Provide comics authoring mechanisms to assist users in follow-
ing genre conventions (C4).
4G
RAPH COMIC GENERATION TECHNIQUES
We introduce techniques for streamlining the generation of graph
comics. We develop a hierarchical clustering algorithm that groups
snapshots into different temporal granularities based on their similarity.
This clustering produces a dendrogram, enabling users to automatically
create comics by selecting the number of panels.
4.1 Visualizing Dynamic Graphs as Comics
A graph snapshot is a static graph representing the state of a dynamic
graph at a specific point in time. We can consider a dynamic graph
as a collection of graph snapshots: snapshots organized in a temporal
sequence, where a snapshot is generated whenever there is a change in
the dynamic graph, such as the addition or deletion of a node or link.
Such a dynamic graph can be naïvely visualized as a data comic by
rendering one comic panel per graph snapshot and then visualizing the
specific snapshot inside its panel as a node-link diagram or adjacency
matrix. We provide continuity between adjacent panels by freezing the
position of nodes from one panel to the next and emphasizing changes—
node and link additions and deletions—using visual highlighting, such
as colors, callouts, and visual effects. The result is a graph comic [4]: a
sequence of panels showing a graph changing over time.
Unfortunately, this naïve approach is not practical for graphs span-
ning long time periods or involving many changes because the resulting
comic will have a prohibitively large number of panels. Furthermore,
large graphs with many nodes and links will yield panels that are so
cluttered that individual changes are hard to spot. Below we describe
practical approaches to address these shortcomings:
Long times: We present a causality-preserving temporal clus-
tering algorithm that combines multiple adjacent snapshots into
clusters to support balancing the number of panels and the amount
of changes in each panel based on the user’s needs; and
Large graphs: We propose graph filtering mechanisms based
on the concept of main and supporting characters so that large
graphs can be reduced to more manageable subgraphs.
4.2 Causality-Preserving Temporal Clustering
We present an algorithm that hierarchically groups graph snapshots in
a dynamic graph based on the similarity between adjacent snapshots
while preserving their causal (temporal) order. Instead of showing every
graph snapshot, the graph comic can show snapshot groups consisting
of the union of several temporally adjacent snapshots. The goal is to
facilitate users precisely adapting the number of panels to show the
resulting comic, from a single panel representing all changes (i.e., a
snapshot group representing all snapshots) to a panel for every individ-
ual change in the graph. We consider four additional requirements for
our algorithm: it must take into account the node and link labels, which
are crucial for the graph data; it should preserve temporal continuity
and consistency, meaning it should provide the same similarity for the
same input; it should factor in multiple numerical attributes for both
nodes and links; and it should not be computationally heavy.
4.2.1 Adjacency-based Hierarchical Clustering
We employ a variant of agglomerative clustering [24] where, instead
of comparing the distance from each graph snapshot to every other
snapshot, we only compare the distances between snapshots that are ad-
jacent in time [85]. In other words, given a dynamic graph
G
consisting
of a sequence of graph snapshots
G
t
for each time
t [0 ...T ]
for the
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976
Fig. 2: Comparison of graph distance metrics for two graphs.
G
t
and
G
t+1
with different labels and attributes with (a) Weisfeiler-Lehman
kernel-based distance, (b) deep learning-based similarity (or distance),
(c) graph edit distance with MCS or unweighted set similarity, and (d)
graph edit distance with weighted Jaccard similarity (our choice). The
thickness of the line is proportional to the node or link attributes.
time period
T
, only graph snapshots at
t 1
and
t +1
are compared (i.e.,
snapshots just before or after the current snapshot). This means that the
temporal order is preserved when graph snapshots are aggregated into
snapshot groups, thus maintaining causality in the resulting comic.
Conceptually, a snapshot group is regarded the same as a snapshot:
it has a time span instead of a point in time representing the first and
last time stamps for its constituent graph snapshots, and its graph is the
union of those constituent graphs. Importantly, the distance metric is
defined similarly for both a snapshot and a snapshot group.
To build a cluster hierarchy of a sequence of graph snapshots (and
snapshot groups), we must provide a distance metric
D(G
1
, G
2
)
that
accepts two adjacent graph snapshots (or groups)
G
1
and
G
2
. We
agglomerate the dynamic graph sequence by progressively selecting the
two adjacent snapshots with the smallest distance and replacing them
with a snapshot group combining them. This means that the number of
snapshots in the sequence will monotonically decrease until a single
snapshot group representing all of the original graph snapshots remains.
4.2.2 Graph Edit Distance with Weighted Jaccard Similarity
Several graph similarity measures exist in the literature. Basic graph
topology measures for unweighted, unlabeled, and undirected graphs
are not good metrics of similarity, as real-world dynamic graphs often
have both node and link attributes that factor into similarity. Next, we
review existing work on graph distance measure metrics and propose
our new distance measure metric used in this work.
We find four types of metrics: graph edit distance (GED), set simi-
larity, kernel-based measures, and deep learning-based measures. GED
calculates a weighted summation of predefined graph edit operators,
such as insertion, deletion, and substitution of a node or link. Although
simple and effective, GED requires human effort to determine each
edit operator and the corresponding cost. Furthermore, existing GED
methods define substitution as a link or node replacement, which is not
suitable for real-world graphs [26]. For example, as shown in Fig. 2
(c), it cannot measure the attribute change of node D.
Set similarity can be used as a graph similarity metric based on the
number of common nodes (or links) in two graphs [41, 73]. The main
advantage of this method is that it has linear computational complexity
and is suitable for real-world applications because of its label awareness.
However, similar to GEDs, it cannot measure attribute changes.
Kernel-based methods using graph topology are another line of
research for graph similarity computation. Examples include path-
based kernels [27, 39], subtree pattern-based graph kernels [48,58], and
Weisfeiler-Lehman optimal assignment kernels [43]. While effective
in measuring the overall similarity over the entire graphs, they often
omit node attributes (R2) and cannot be used for varying aggregation
levels (R2). In addition, as shown in Figure 2 (a), it may lose the label
information (e.g., treating link (A, E) and (E, F) as the same one).
Deep learning-based methods [47, 82] have been developed to over-
come the heavy computational costs in measuring graph similarity.
However, the explainability of deep learning methods remains an open
problem, so we do not use them in this work, as our graph comics need
multi-perspective storytelling (R2) with solid reasoning (R3–R4).
After reviewing existing metrics and their characteristics, we find
that traditional methods, including GED and set similarity, are the
most appropriate metrics for our purpose. However, they cannot be
directly used for our work, as they are not able to measure attribute
changes. As such, we develop an enhanced method with GED and
weighted Jaccard similarity. Our method calculates vector-form Jaccard
similarity [62] for each common node or link. For example, as shown
in Fig. 2, we find common elements with labels (e.g.,
D
or
(B, E)
) and
then calculate vector-form Jaccard similarity for the attributes of each
common element. As a result, we can obtain the degree of changes per
link or node (e.g., change of attributes for node D), as in Fig. 2 (d).
5T
HE DG COMICS SYSTEM
We design DG Comics to meet the challenges and satisfy the require-
ments from Sec. 3. Our approach outlined in Sec. 4 efficiently manages
complexity (R1) for automatic graph comic generation. The algorithm
output, visually represented as a dendrogram (Sec. 5.1), allows users
to interactively choose an aggregation level for story fragments, which
can then be depicted as comic panels (Sec. 5.2). Each comic panel
represents a story fragment, and the node-link diagram in the panel
visualizes changes during that interval (R4). Users can build their own
stories by inspecting potential main and supporting characters (R3) with
graph evaluation metrics (R2), such as node centrality, node degrees,
and adjacency (Sec. 5.4), as well as individual graphs at different time
points (Sec. 5.3). DG Comics facilitates the observation of communi-
ties (Sec. 5.5) where characters are involved (R2). Finally, it enables
editing based on the language of comics (Sec. 5.2), including fonts,
motion lines, captions, and layouts (R5). DG Comics employs Next.js
for the frontend, FastAPI for the backend, and the D3 library [14] for
visualizations, including computation of node-link diagram layouts.
The source code is available at github.com/joohe-e/DGComics.git.
5.1 Summary View
Comics are made of strips of one or more panels [50, 64], each de-
picting a portion of the narrative and the sequence typically showing
temporal progression. In a graph comic, each panel contains a snapshot
of the dynamic graph sequence (either as an individual or a group) and
a caption that provides information about the snapshot. The Summary
View (Fig. 3A) facilitates the automatic generation of a series of pan-
els via a dendrogram that visualizes the hierarchical clustering result
(Sec. 4). Each leaf node of the dendrogram refers to a graph snapshot at
a specific time point. The X-axis indicates the time from the beginning
to the end of the data, and the Y-axis notes the normalized similarity be-
tween two adjacent snapshots from 0 to 1. The more similar the groups
or individual snapshots are, the lower their position is connected, as
the distance is shorter. A horizontal dashed line, called a depth slider,
partitions the dendrogram into multiple clusters, visually encoded with
different colors. The number beside the line indicates the number of
clusters formed at that depth level.
Users can select the number of panels to include in the comic strip
by moving the depth slider (R1). As users adjust the slider, the clusters
are colored differently. They can generate the comic strip by clicking
the
GENERATE
button. Each branch selected by the depth slider returns
the characters that change the most over the timespan corresponding to
the cluster (R3). For example, if a user clicks
GENERATE
after positioning
the depth slider as shown in Fig. 3A, three panels are created in the
comic view, each representing the assigned clusters (green, purple, and
orange). DG Comics offers two options for constructing subgraphs with
the ego being the resultant main character(s): a 1.0-level ego network,
which includes the ego and its 1-degree alters, and a 1.5-level ego
network which also includes the ties between the ego’s alters. Users
choose an option based on what relationship they want to present (R2).
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977
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Graph Comic
Timeline ADD REPL
2011
2012
2014
2015
2016
Level 1.5
Daniel A. Keim
Supporter Top 15% Highlighter Top 5%
Main Character
Daniel A. Keim 7 24 14.44
Olivier Thonnard 6 6 6
Adam Sah 5 5 5
Jefferson
Amstutz
7 7 7
Erik Trostmann 3 3 3
Christoph
Wimmer
4 4 4
Jan Kretschmer 3 5 4
Markus Höhn 12 12 12
Aude Oliva 6 7 6.5
Kevin A. Roundy 7 7 7
Robert Kosara 1 1 1
1 2 3 4
30
Supporting Character
EDIT
Tobias Schreck 10
Dominik Sacha 6
Michael
Behrisch 0001
5
Enrico Bertini 4
Halldór
Janetzko
4
Dominik Jäckle 4
Oliver Deussen 3
Christopher
Collins 0001
3
Juri
Buchmüller
3
Mennatallah El-
Assady
3
Matthias Kraus 3
1 2
30
Daniel A. Keim
Tobias Schreck
Enrico Bertini
Halldór Janetzko
Michael Behrisch 0001
Dominik Sacha
Daniel A. Keim
Tobias Schreck
Michael Behrisch 0001
Daniel A. Keim
Daniel A. Keim
Daniel A. Keim
Daniel A. Keim
Summary Node Attribute Community
GENERATE
3
Search node
Search node
Sankey Node Min. Max Avg Class Sankey Node Weight
Enrico Bertini
Daniel A. Keim
Tobias Schreck
Christian Rohrdantz
Daniel A. Keim
Tobias Schreck
Dominik Sacha
Daniel A. Keim
Tobias Schreck
Dominik Sacha
Daniel A. Keim
Dominik Sacha
Tobias Schreck
Daniel A. Keim
Dominik Sacha
Tobias Schreck moved Graz University of Technology in 2015.
Dominik Sacha actively collaborated with Daniel Keim during his Ph.D.
                   Howe
ver, since the transition to an industry role,
         he st
opped publishing papers at Visualization conferences.
FC
Mental map preservation
M
Community changes
O
A
G
H
B
D E
X
delete
color
size
STcolor
stroke
highlight
opacity
label
image
Lsize
cluster
Fig. 3: DG Comics overview. DG Comics offers (A) a Summary View, (B) sliders for filtering and highlighting nodes, (C) a Graph Comic View, (D)
Main Character and (E) Supporting Character tables, and (F) a Timeline View. Users can switch to (G) the Node Attribute Table or (H) Community
View using the tab. It supports (M) mental map preservation by fixing nodes across displays, and (O) community changes using bubble sets.
Union T1
Union T2
T2T1
T1 T2
U
Added
=T2-T1
Deleted
=T1-T2
Maintained
=
T1 T2
U
Selected Panel
Fig. 4: Data abstraction and presentation. Each cluster below the
depth slider (left) represents a panel (right) through a set operation of
two subcluster snapshots. The aggregate graph snapshot is computed
by the union of graphs for timespans T1 and T2.
5.2 Graph Comic View
The Graph Comic View allows users to edit graph comic templates
while managing large dynamic graphs efficiently. To reduce visual
clutter and enhance readability, we provide an option to keep only the
top N percent of alters based on link weights visible. Users can control
this level of visibility using supporter slider, and further highlight
specific alters with the highlighter slider. Both sliders can be adjusted
simultaneously before users begin editing (Fig. 3B).
5.2.1 Visual Design
We create a node-link diagram for each panel as the union of the
panel’s children (or the snapshot itself for leaf nodes). Each panel
visualizes the changes between two consecutive children snapshot
groups, highlighting added nodes and links with a neon effect and
deletions with dashed strokes (R4). We abstract the data for each
change using set operations: subtracting the former timespan (T1) from
the latter (T2) yields the added subgraph, while the reverse reveals the
deleted subgraph (Fig. 4). Supporting characters are defined as nodes
with greater weight in their relationship with the main character. We
emphasize main and supporting characters by thickening the stroke of
the nodes, applying diverging colors, and including labels; their colors
can be changed using a color picker.
Panel Layout. Since each panel contains a narrative, its size and
placement are crucial for conveying the message. Specifically, viewers
can infer event duration from the width of the panel [64] and determine
the chronological order based on the panel’s position in the strip, where
time proceeds from left to right and top to bottom [50, 64]. DG Comics
is designed to generate an appropriate panel layout. The layout algo-
rithm first calculates the number of rows, or tiers, by taking the square
root of the total panel number specified by the user, ensuring each panel
maintains a minimum height. For example, with 16 panels, DG Comics
assigns 4 tiers. It then allocates panels to each tier to balance the sum
of their time intervals. The comic is partitioned using this structure,
with gutters between panels for clarity. This approach normalizes time
points across tiers, assuming that panels with more time points convey
more information, and encodes event duration into panel size [4].
Captions. DG Comics provides a caption template for each panel in
the main character’s point of view to assist storytelling. Each caption
consists of three clauses about a summary, major change, and the most
influential relationship. The summary describes expansions, contrac-
tions, and constancy based on the difference between the graphs before
and after. It includes a prepositional phrase in front to indicate the
timespan and reference time for a change. Then we compare the total
number of nodes added, deleted, and preserved, and state one case that
is the maximum. Lastly, we select the node(s) that have the highest
link weights and mark whether the main character had obtained, lost,
strengthened, or weakened its relationship with those nodes. A set of
clauses is created per main character, but each clause can be combined
into one with multiple subjects if different main characters share the
same content. When the panel portrays a particular time point, it simply
reports the strongest relationship.
5.2.2 Interaction Design
DG Comics offers diverse editing interactions to enhance flexibility
in content creation (R5). Users can manage the general format of the
canvas and graph drawing using the toolbar (Fig. 3C, top-right) in the
Graph Comic View and adjust the details, such as text and graph styles
within the canvas using the toolbox that appears on the right side of
Graph Comic View when users interact with the element.
Canvas Editing. We define an editable component of the panel as a
canvas. DG Comics includes three canvas types: graph, text, and image.
An auto-generated panel contains one graph and two text canvases—a
caption and temporal information. Users can move, resize, and rotate
a canvas by interacting with the four sides and corners of the canvas,
respectively. The canvas editing can be done using the toolbar on the
upper right corner of the Graph Comic View (Fig. 3C). Clicking the
button reveals a list of canvas types that can be added to the workspace.
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978
Hanspeter P ster 0 0.38 0.06
J. Edward Swan II
0 0.37 0.12
Daniel A. Keim
0 0.37 0.03
David H. Laidlaw
0 0.36 0.03
Xiaoru Yuan
0 0.36 0.03
W. Brent Seales
0 0.35 0.18
Hangzai Luo
0 0.35 0.18
Christy Jie Liang
0 0.35 0.09
Summary Node Attribute Community
eigenvector_centrality
Search node
Node Attribute Distance Min. Max Avg
Hanspeter P ster 0 0.38 0.06
J. Edward Swan II
0 0.37 0.12
Daniel A. Keim
0 0.37 0.03
David H. Laidlaw
0 0.36 0.03
Xiaoru Yuan
0 0.36 0.03
W. Brent Seales
0 0.35 0.18
Hangzai Luo
0 0.35 0.18
Christy Jie Liang
0 0.35 0.09
1 2 3 4 5
30
eigenvector_centrality
Search node
Node Attribute Distance Min. Max Avg
C
B
A
Fig. 5: Node Attribute Table. This table shows a list of all the nodes
and their values over time.
One distinct option is
Background
, which inserts a background image
into the graph canvas. To add a graph canvas, users select the timespan
on the Summary View by brushing before pressing the
Graph
button.
This will generate the change summary of an extracted main character
during the chosen timespan as explained in Sec. 4.1. The
Text
option
enables users to create an empty text box, while the
Image
option offers
preset icons such as speech balloons and arrows and supports importing
external images via the button. Users can simply delete, and move
back and forth the focused canvas by clicking , , and buttons.
Graph Drawing. DG Comics provides multiple modes for generat-
ing the graph layout before manually repositioning. When pressing the
button on the toolbar, buttons for three modes appear. The default
mode is a
Basic
force-directed graph that uses the concepts of repulsion
and attraction to place the relevant nodes closer and irrelevant nodes
further. Derived from this graph structure, the
Filled
mode reduces
empty space to a minimum in the way of multiplying scale to the rel-
ative position. This mode can be useful to increase the readability of
nodes by preventing overlapping. The
Fixed
mode supports mental
map preservation by fixing node positions across the panels (Fig. 3M).
To achieve this, we compute a force-directed layout of the union of all
nodes and links in the dynamic graph over its full timespan. This is
useful for tracking changes in individual panels and facilitates the easy
detection of nodes and links being added or deleted.
Text Editing. When the text canvas gets focused, it triggers a
movable toolbox to appear on the upper right side of the Graph Comic
View. Users can drag the text content for selection and change it to be
bold, italic, or underlined. The typeface and size are also adjustable.
The background and border of the text canvas can be on and off. We
integrate an LLM (
Mixtral 8x7B
[36]); clicking the button will
use the LLM to improve the caption automatically.
Graph Element Editing. In the default mode, users can zoom in
on the graph canvas by scrolling and pan by dragging the background.
Each node is movable, and its style, along with the link, is editable.
DG Comics offers three methods for selecting multiple nodes: users
can individually click on the nodes; employ the lasso tool from the
graph comic view toolbar for easier group selection; or use a supporting
character table by clicking on a character row and then the
EDIT
button
(Fig. 3E). Once the selection is made, group movement of the nodes
is available, and a toolbox appears, allowing customization of styles.
The node-level toolbox provides functions for changing color, size,
and stroke properties, adjusting opacity, applying highlights, adding
labels, inserting images, deleting nodes, and clustering nodes. Links
can be edited by a direct click, and the link-level toolbox offers similar
functions, except for labeling, grouping, and image insertion. It also
features the ability to change markers, particularly useful for directed
graphs. Users can change the color and opacity of a cluster created
either by manual grouping or by selecting from the Community View
(Sec. 5.5). Once all the editing is done, users can export the final
product as PNG, JPG, or PDF via the button.
5.3 Timeline View
We can express temporal changes not only with the symbolic repre-
sentation but also through the separation of panels. The Timeline View
(Fig. 3F) lets users choose a transition style by listing static graphs of
different time points. Users can scrutinize graphs included within the
chosen time span and select one to add to the canvas (R4, R5).
The Timeline View provides two functions for generating a series
of graphs: adding and replacing the selected panel. The
ADD
option
enables switching focus from overview to detail by attaching the graphs
of certain points in the bottom right corner. We use the same layout
computation for the
ADD
option to embed multiple panels in a limited
space while not covering the root panel completely. The
REPL
option
replaces the selected panel and arranges new panels in temporal order.
5.4 Tables
We provide three tables in DG Comics to select node attributes and
characters to display in the graph comic. The common features of the
tables are sorting by values and searching by names.
The Node Attribute Table (Fig. 5) facilitates node exploration and
assists in manual main character selection by providing comprehensive
attribute values and chart thumbnails (R3). Users can switch to this
table by clicking the second tab above the Summary View (Fig. 3G).
The line chart thumbnail tracks the value over time; lines are dashed if
data is missing (Fig. 5B). To prevent possible bias from inconsistent
scales, we also mark minimum, maximum, and average values. Users
can examine different attributes of a node—e.g., the total number of
links, PageRank, and centrality—by selecting the option in the top
left corner of the view (Fig. 5A). The heatmap presents the degree of
dissimilarity between the consecutive time points, meaning that the
color gets darker as the distance computed with our modified Jaccard
index increases (Fig. 5C). Using this thumbnail, users can capture
moments with significant change. When selecting a main character on
the attribute table, the dendrogram is re-created to reflect the subgraph-
level change (R2). This means that each time node of the dendrogram
represents the subgraph grounded in the selected node (Fig. 3A).
The character tables (Fig. 3D, E) manage the nodes depicted in the
graph comic. The list of nodes is updated whenever users click the
graph canvas to focus. Users can either add or delete nodes by selecting
or deselecting the leftmost checkbox. While the Main Character Table
(Fig. 3D) shows all the nodes as candidates for the main character, the
Supporting Character Table (Fig. 3E) lists the neighbor nodes that are
linked with the currently selected main character (R3). The minimum,
maximum, and average values in the Main Character Table reflect the
attribute selected in the Node Attribute Table. The total weight of links
to a node in the selected time span is an indication of the importance
of the node (Fig. 3E). The Class column in the Main Character Table
enables direct manipulation of graph element styles. By entering the
tag name as input and clicking on the Class header, users can open
the CSS editing view to redefine the properties of the corresponding
nodes (Fig. 3D). The
EDIT
button in the Supporting Character Table is
explained in Section 5.2.2.
5.5 Community View
Identifying the communities in which nodes are involved (i.e., commu-
nity membership) and tracking their evolution is crucial for a deeper
understanding of their relationships (R2). For instance, if two nodes
from different communities merge into the same community, we can
assume that their relationships become stronger; if they split, the rela-
tionship becomes weaker. A Sankey diagram is a suitable approach to
illustrate such flow by representing communities as nodes and transi-
tions between them as links. To differentiate their nodes and links from
graph elements, we call them community nodes and paths. However,
its readability significantly decreases with the numerous overlapping
paths and varied sizes of community nodes. Given that the narrative of
the graph comic is character-driven, we decided to highlight only the
communities of the chosen characters in this work. Moreover, we re-
designed the Sankey diagram to enable fair inspection and comparison
of community changes associated with the characters. Fig. 6 shows our
Community View, re-designed in this work.
In a traditional Sankey diagram, the size of a community node is
proportional to the size of the community, making it difficult to observe
transitions for characters in smaller communities. To overcome this
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Summary Node Attribute Community
Louvain
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
RESET SELECTION RESET PATH
ain
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
Fig. 6: Community View changes when communities are selected,
as shown in the left inset, where red and blue colors mean Keim and
Schreck, respectively, whose communities diverged since 2014.
obstacle, we encode the community size into a sequential color scheme
while normalizing the community node size. Users can choose a cluster
color in a color palette by selecting the second checkbox on the ‘Sankey’
column (Fig. 3 D, E). We set the time as the x-axis and place community
nodes from top to bottom based on their community size. To connect
the community nodes for a selected character, we adopt a circular
shape with padding inside the grid cell so that the path can enclose it
smoothly. As a result, the Community View displays the colored path of
community evolution for the selected character while blurring nodes
outside of this path as Fig. 6 shows.
The Community View enables users to detect six types of events
in community evolution (Fig. 7): growth, contraction, merging, split-
ting, birth, and death [56]. Since DG Comics supports character-based
exploration for generating narratives, the community is primarily iden-
tified by the character. Therefore, the start and end of the path indicate
the birth and death of the character’s community within the dataset’s
time span. Users can determine growth and contraction through color
changes and the vertical position of the circle across different years.
Furthermore, by selecting multiple characters, users can observe how
distinct communities evolve to merge and split at specific time points
throughout the time span. Notably, the visual patterns depicted by
multiple paths can illustrate how the communities evolved through
various events (e.g., splitting, merging, homogeneity, heterogeneity)
over different time periods as Fig. 7 shows. After exploring community
evolution, users can add a cluster to the canvas by clicking the commu-
nity node (Fig. 3O). DG Comics uses Bubble Sets [21] to highlight the
group of characters involved. The
RESET PATH
button (upper left) clears
all highlights from the Community View, while the
RESET SELECTION
button
removes all the clusters from the graph comic. The default setting dis-
plays the communities detected by the Louvain algorithm, but another
option is available in the drop-down list, such as affiliations of authors
in the case of Vispubdata.
6E
XAMPLE
We demonstrate the use of DG Comics in scientometric analysis with
data from the visualization community. We use Vispubdata [35], a
dataset containing information on 3,620 papers from IEEE Visualiza-
tion conferences (InfoVis, SciVis, VAST, and VIS) from 1990 to 2022,
including conference, year, title, paper type, authors, affiliation, key-
words, and number of citations. This dataset is frequently used for
cross-checking in dynamic network research and is highly relevant to
the visualization community. To create graph comics with compelling
storylines, we constructed author networks, where nodes represent
authors and links represent collaborations based on co-authored papers.
6.1 Analysis
We start with the default dendrogram, which computes the dissimilarity-
based hierarchy of the entire dynamic graph, adjusting the depth slider
to segment the time into nine divisions. This process generates nine
panels, offering a visual narrative of the data, which in this case, is
the collaboration network in VIS papers. We select 1.5-level ego
networks to observe connections between the neighbor nodes as well.
Birth
Death
Contraction
Growth
Splitting
Merging
Heterogeneity
Homogeneity
Fig. 7: Community evolution. Six archetypes of community evolution.
The auto-generated comic reveals DANIEL A. KEIM as a central figure
in two distinct periods, 2006–2011 and 2012–2016, with the network
expanding in the earlier period and contracting in the latter. We can now
generate a graph comic from Keim’s perspective for closer examination.
By opening the Node Attribute Table and searching for “Keim, we
detect the high dissimilarity (Fig. 5C) and peak in eigenvector central-
ity (Fig. 5B) between 2011 and 2012, which suggests transformations
in Keim’s collaborative patterns. With this insight, we create a new
dendrogram for Keim and regenerate the graph comic, which unveils
two main clusters before and after 2011, indicating major events in the
network’s evolution (Fig. 3A). We move the sliders to filter out impact-
ful relationships involving the top 15 percent of total collaborations and
highlight the top 5 percent (Fig. 3B). To examine changes closely, we
add the graph canvas by brushing 2011 to 2020 from the Summary View
and insert the individual graphs of 2011 and 2012 from the Timeline
View (Fig. 3F). The tool’s features allow us to preserve the mental map
(Fig. 3M), helping us to capture the establishment of new relationships
and community shifts. Notably, we observe a strengthening of rela-
tionship with T
OBIAS
S
CHRECK
from 2011 to 2020, prompting us to
further compare the dynamics between Keim and Schreck.
In the Community View, we color-code Keim’s (blue) and Schreck’s
(red) communities and draw paths to visualize their changes over time
(Fig. 6). Despite multiple collaborations, their communities have di-
verged since 2014 (Fig. 3O), reflecting the impact of Schreck moving
to Graz University of Technology in 2015. This divergence, despite
ongoing collaboration, illustrates nuanced dynamics common in aca-
demic networks. Further exploration reveals D
OMINIK
S
ACHA
as a
significant yet eventually diverging connection, mirroring the evolution
of community affiliations and the impact of career milestones such as
Ph.D. completion and industry employment. By juxtaposing Sacha’s
trajectory with Schreck’s, we enrich the narrative, offering compara-
tive insights into how individual careers and collaborations shape the
broader academic community. This scenario demonstrates the tool’s ca-
pacity to dissect, visualize, and narrate complex relational data, making
it an invaluable resource for understanding dynamic networks.
7E
VALUATION
To explore the usefulness and effectiveness of DG Comics, we con-
ducted a comprehensive user study. This study includes usability tests
and interviews to gather valuable insights from participants and in-
depth feedback from experts to validate its versatile application.
7.1 User Study
We conducted a controlled user study to evaluate DG Comics. The
study consisted of a pre-study questionnaire, a tutorial session, graph
comics creation, and a post-questionnaire with interviews. We present
both quantitative ratings and qualitative insights from the interviews.
We used the COW trade dataset [9,10] of international trades from 1870
to 2014, covering 207 unique nations. Using this data, we built a di-
rected graph of international trade where each node and link represents
nationality and corresponding trade (in U.S. dollars), respectively.
7.1.1 Participants
We recruited 13 participants from a university (4 females) aged from
23 to 29 (
M = 25.4, SD = 2.2
). The number of participants is consis-
tent with prior work [17, 34, 54], aligning with recommendations for
usability testing. They were graduate (
n = 8
) and undergraduate (
n = 5
)
students from electrical engineering (
n = 4
), computer science (
n = 5
),
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980
1
5
7
2 1 10
2 7 4
3 9 1
21 6 4
62 4 1
321 3 4
7 6
321 4 5
strongly
disagree
strongly
agree
Q1) I was able to efficiently create graph comics using the system.
Q2) I could effectively express the story I wanted using the system.
Q3) Using the system was enjoyable.
Q4) The system helped in discovering new insights or perspectives from the data.
Q5) The graph comics created with the system were easy to understand.
Q6) I think the graph comics were represented reasonably.
Q7) I think the graph comics were aesthetically presented.
Q8) I am satisfied with the outcome.
Overall Ratings of DG Comics
4.46
(0.66)
4.46
(0.52)
4.00
(0.91)
4.62
(0.77)
4.15
(0.69)
3.85
(0.55)
3.31
(0.85)
3.54
(1.33)
IQR Median
20 40 60 80 1000
67.88 (7.76)
System Usability Scale
Fig. 8: DG Comics ratings. Overall ratings (top) and SUS score (bottom).
Values are means and standard deviations (in parentheses).
and artificial intelligence (
n = 4
). All participants had experience in
analyzing data using Python and visualizing it with Python, Excel, and
PowerPoint. Most of them had experience dealing with graph data (e.g.,
traffic, sensor network, etc.), except for three participants who were
aware of graph data but lacked experience in processing it. Participants
were compensated $22 (USD) for 2 hours of study. The study was
reviewed and deemed exempt by our Institutional Review Board.
7.1.2 Procedure
The study began with a pre-study questionnaire that included: (1)
demographic information, such as age, gender, and education level;
(2) participant experience with data analysis, data visualization, and
graph data; and (3) participant familiarity with graph comics, the trade
dataset, and events of world history relevant to the study. We then
explained what a data comic is and how it can be applied to graph
data. We conducted a 20-minute tutorial on how to use DG Comics and
interpret the visual representations. Then participants were given 5 to
10 minutes to explore the system on their own and ask questions about
its functions. Note that we used Vispubdata [35] for the tutorial session
to prevent learning effects on the dataset used in the experiment.
After participants felt comfortable with the tool, we introduced them
to an international trade dataset spanning from 1930 to 1960. They
were tasked with using DG Comics to craft an engaging graph comic,
highlighting the evolving relationships between nations. To aid in their
task, we provided access to a Wikipedia page titled Timelines of Modern
History, which includes sections such as Timeline of the 20th Century
and List of Wars. The purpose was to accommodate varying levels of
familiarity with world history among participants. While participants
could use search engines to gather information and craft their storylines,
we prohibited the use of any comprehensive historical resources, such
as videos, that might present a finished interpretation of events.
Assistance was offered solely at the request of participants, ensuring
that their interaction with the system remained independent. Partici-
pants used Google Chrome in full-screen on two 32-inch monitors with
a resolution of
2560 × 1600
pixels—one for information search and the
other for the actual comic creation. Participants were allotted 1 hour
and 10 minutes for this task, during which their interactions with the
system were screen-recorded for further analysis.
Following the comic creation phase, we asked participants to fill out
a post-study questionnaire. This questionnaire gauged their opinions
on the usability, overall experience, and the helpfulness of different
views within DG Comics, as well as their satisfaction with their final
product, using a 5-point Likert scale. We concluded each session with a
semi-structured interview to explore the reasoning behind questionnaire
responses, the narratives constructed, and comprehensive feedback on
the system’s functionality and user experience.
7.1.3 Quantitative Results
We adopted a 5-point Likert scale to assess the overall experience of
the system (Q1-Q4) and the output that participants generated (Q5-
Q8). We used the System Usability Scale (SUS) designed by Brook
et al. [15] for global evaluation of systems usability. Interpreting the
Likert scales as ordinal, we report the quantitative results in a discrete
visualization style [67], the histogram [32, 84] with medians (as blue
lines) and quartiles (in gray areas). We also provide means and standard
deviations considering intervalist views [19].
As Fig. 8 shows, participants felt positive about their experience
using the system (Q1-Q3). In particular, they highly valued discov-
ering new insights and perspectives from the system (Q4). They all
agreed that the comics they made were easy to understand (Q5-Q6).
However, aesthetics and satisfaction with the output varied among the
participants, likely due to the discrepancy between their editing skills
and expectations toward their outcomes (Q7-Q8). The SUS score is
acceptable (
M = 67.88, SD = 7.76
); compare this to the average of 68.2
from 273 prior studies [8].
7.1.4 Qualitative Results
We analyzed participants’ responses and video recordings associated
with the scores. Here we present these qualitative results.
The summary view supports efficient story generation. Most
participants reported that the overview with the dendrogram helped
generate stories (
MD = 4, M = 4.15, SD = 0.99
). Even though most
of them had little knowledge of the data, they were able to understand
the summary of changes in relationships at a glance. They used the
auto-generated comic as a starting point. The hierarchical clustering
effectively grouped similar time points to depict relevant stories into
one panel, allowing participants to choose how detailed or brief to
express the story by adjusting the level (P5). Clusters gave a hint to
users where to focus; for example, P9 after detecting groups before and
after World War II, focused on the specific branches for storytelling
the events while P12 scrutinized the clusters as a chunk of changes.”
The community view effectively aids in inferring relationships
between nodes and events. Participants used the community view
to check the relationship between nodes (
n = 7
). Some participants
displayed a node’s community to see other nodes in the same cluster
and used this information to structure the graph (P3, P7, P8, P11,
P12). P12 excluded some countries such as the U.S. from the graph
since their consistency in the relationship with the United Kingdom
suggests no special event happening. They instead captured insights by
inspecting the community visualization. For example, the comparison
between the community evolution paths of different countries provides
an implication for a significant event when there is a split or a merge.
P6 searched the interaction between Poland and Germany since their
paths overlapped during the 1930s.”
Participants wanted to use the system with their own data. All
participants indicated that the system helped them effectively express
the story (
MD = 4, M = 4.46, SD = 0.52
). Some commented on chal-
lenges in visualizing dynamic graphs, especially for big and complex
data (P1, P5). However, using DG Comics, they were able to convey
changes in the relationship between nodes over time regardless of fa-
miliarity with the data (
n = 7
). P7 noted that it became much easier to
narrate the story of network evolution because the system helps with
the hardest part, [which is] selecting the main character. P10 said that
although he did not have an aesthetic sense, [he liked that he could]
make a satisfactory product within an hour. Some participants brought
up the point that the convenience of the system does not only rely on
visualization but also on interpretation of the data. If the series of time
points are clustered, users can think about the reason and assume the
circumstance (P5). P1, who conducts research on tracking individuals
using ultra-wideband data, said, it would be very helpful to [be able
to] detect when interference between two groups occurs.”
7.2 Expert Feedback
We conducted online interviews with six experts across various do-
mains, who were invited via emails with consideration of domains and
expertise. Each expert has a Ph.D. degree and 8 to 19 years of work
experience in their respective fields. Their areas of expertise include
Communication (E1: 19 years, E2: 8 years), History (E3: 12 years),
Design (E4: 12 years), Management Information System (E5: 8 years),
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kim ET AL.: DG COmiCS: SEmi-AUTOmATiCALLY AUTHORiNG GRAPH COmiCS FOR DYNAmiC GRAPHS
and Economics and Finance (E6: 11 years). Before interviews, we sent
them the information on the interviews (e.g., goal, duration, expected
outcomes), a demo video, and a website link to the deployed system.
The interviews began with a short system demonstration, followed by a
Q&A session for any clarifications. Once interviewees were familiar
with the system, we proceeded to discuss its various aspects.
All appreciated the usefulness of the tool in telling a story using an
approachable format (i.e., comic). They reported various strengths of
DG Comics, including automated story extraction, rich functionality
in editing for story presentation, and novelty of the approach in DG
Comics. E1 lauded the system is capable of extracting outstanding
stories with the corresponding timespan, streamlining the research
process for data journalists. E4 and E5 also noted that laypeople,
such as marketers or end-user creators, can use the tool to create and
present a story with data that would otherwise have been inaccessible
to them. E1 and E2 raised a similar point, that DG Comics enriches
users’ insights by providing the change in dynamics between characters.
E3 even expressed his willingness to incorporate the system into his
curriculum of political history. Given the possible impact of politics
on economics, I believe students can gain insights into economic re-
lations, such as international trade, by comparing them with political
knowledge through a comic." E5 and E6 underlined the contribution
of autogenerated output to efficient decision-making. Specifically, E5
commented that autogenerated output from dynamic graphs will help
users in management who need to analyze dynamically changing re-
lationships among companies—“an intuitive understanding of supply
flows and the impacts of subcontractor changes can help users make
strategic decisions in supply chain management. E4 noted that many
people need to create and present stories from data but are often frus-
trated by challenges brought by new and complex data. In this situation,
he expected the tool would be very helpful. He highlighted the novelty
of the approach that DG Comics provides, stating, ... I give 10 out of
10 for novelty because I have not seen this type of system and support
from a complex data storytelling perspective.
All experts estimated that the target user spectrum is broad, encom-
passing a diverse array of individuals. With the automatic generation
of comic templates and robust editing functions for crafting user-driven
stories, experts believe DG Comics meets the needs of a wide range
of users, from novices to professionals. They also commented that
usage scenarios with DG Comics may vary depending on the user’s
level of expertise and goals, even among the same expert group. For
instance, E1 predicted that novice users would primarily utilize the
Graph Comic View, whereas experts would navigate between views
to gain a deeper understanding of the data. Additionally, E4 men-
tioned that researchers and data analysts would review the summary
of changes from a generated comic to select data before beginning their
analysis, yet if they already know the key points, their focus would shift
toward authoring to convey their message.
Some experts expressed concerns about the steep learning curve due
to the comprehensive features implemented in the system. For example,
E2 and E4 expressed a concern that this system seems to require some
learning time before users fully enjoy the system functions. They
suggested creating a lite version of the system with fewer features for
beginners. On the other hand, some other experts, namely E2, E5,
and E6, indicated that experts may need additional features that help
users automatically process unstructured, complex graph datasets. E5,
in particular, mentioned that there is a significant amount of dynamic
graph data in companies, but only data scientists can pre-process the
data for analysis and presentation purposes—“If the tool included an
interface for processing raw dynamic graph data, it could have a more
substantial impact, given the number of employees in enterprises who
need to analyze, monitor, and present stories with large graph datasets.
8LIMITATIONS AND FUTURE WORK
DG Comics provides a flexible and intuitive authoring environment for
graph comics creation. However, the numerous functions and steps in
the authoring process may cause newcomers to face a steep learning
curve. To mitigate this, providing an interactive tutorial and a help
menu that supports keyword search and mouse-over explanations can
be a reasonable solution. We also consider providing a function for
adjusting the level of interface complexity. Since our user study may
not capture the full diversity of all potential users and was conducted in
a limited amount of time, further investigation, such as a longitudinal
study, would help ensure ecological validity in the complexity reduction
of visualization and storytelling tools across different users.
The wide range of domain experts we interviewed suggests that
DG Comics has potential for diverse applications. However, ques-
tions about data preprocessing remain. Expert feedback reveals that
real-world network data, such as transactions between companies, com-
munications within and between departments, and distribution channels,
are often unstructured and unprocessed. To address this, automated
preprocessing of raw data into a compatible format is the next crucial
step for DG Comics to enhance its versatility. As a prototype, DG
Comics does not provide automated methods for extracting interesting
patterns from node and link attributes (e.g., anomaly detection for time
series [66]). Future work may focus on developing automated methods
to improve the utility and generalizability of storytelling tools. We also
expect the tool can be extended to incorporate datasets from biology
(DNA, proteins), software engineering (call graphs, code dependencies,
developer activities), and road networks with varying traffic situations.
While the dendrogram effectively encodes each snapshot as a leaf
node, it faces scalability limitations with large dynamic networks con-
taining numerous snapshots. To enhance readability, we need to explore
abstraction techniques [44, 52, 57] such as collapsing and expanding
snapshots, despite the additional interaction they introduce. Currently,
the prototype supports only a linear cut on the dendrogram. Though
users can create additional canvases by brushing, enabling multiple
cuts at different abstraction levels would offer greater flexibility in gen-
erating templates automatically. A future system supporting automated
story generation with multiple cuts at different levels would improve
both scalability and usability for real-world applications.
Besides, examining more sophisticated filtering techniques and com-
munity discovery methods would significantly enhance functionality.
DG Comics offers sliders for intuitive filtering, allowing users to high-
light important nodes based on link weights and reduce visual clut-
ter. However, adopting more advanced filtering techniques should be
inspected to prevent potential biases [60] from selecting subgraphs
centered on the main character. The Community View displays commu-
nities detected by the Louvain algorithm or predefined communities
such as affiliation. To enrich narratives, future work could incorporate
advanced community discovery methods for temporal networks [61],
enabling the tool to suggest more intriguing and comprehensive stories.
9CONCLUSION
This paper presents DG Comics, a semi-automatic authoring tool for
graph comics based on identifying notable changes in a dynamic
graph. Our approach automatically extracts meaningful events us-
ing a causality-preserving hierarchical clustering method. With the
algorithmic output, users can adjust granularities by manipulating the
aggregation level and refine aesthetics by making editorial decisions
down to minute details in comic strips through an intuitive user interface.
Our user evaluation and expert feedback demonstrate the usefulness of
the system, providing promising areas for future work.
A
CKNOWLEDGMENTS
This work was supported by the National Research Foundation of Korea
(NRF) grant funded by the Korea government (MSIT) (No.RS-2023-
00218913, No. 2021R1A2C1004542), by a grant of the Korea Health
Technology R&D Project through the Korea Health Industry Develop-
ment Institute (KHIDI), funded by the Ministry of Health & Welfare,
Republic of Korea (grant number:HI22C0646), and by the Institute of
Information & Communications Technology Planning & Evaluation
(IITP) grants (No. 2024-00360227-Leading Generative AI Human
Resources Development, No. 2020-0-01336–Artificial Intelligence
Graduate School Program, UNIST). Niklas Elmqvist was funded by
Villum Investigator grant VL-54492 by Villum Fonden.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 31, NO. 1, JANUARY 2025
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REFERENCES
[1]
J.-w. Ahn, C. Plaisant, and B. Shneiderman. A task taxonomy for network
evolution analysis. IEEE Transactions on Visualization and Computer
Graphics, 20(3):365–376, 2014. doi: 10.1109/TVCG.2013.238 2, 3
[2]
D. Archambault, T. Munzner, and D. Auber. Grouseflocks: Steerable
exploration of graph hierarchy space. IEEE Transactions on Visualization
and Computer Graphics, 14(4):900–913, 2008. doi: 10.1109/TVCG.2008.
34 2
[3]
B. Bach, N. Henry-Riche, T. Dwyer, T. Madhyastha, J.-D. Fekete, and
T. Grabowski. Small MultiPiles: Piling time to explore temporal patterns
in dynamic networks. Computer Graphics Forum, 34(3):31–40, 2015. doi:
10.1111/cgf.12615 2, 3
[4]
B. Bach, N. Kerracher, K. W. Hall, S. Carpendale, J. Kennedy, and N. H.
Riche. Telling stories about dynamic networks with Graph Comics. In
Proceedings of the ACM Conference on Human Factors in Computing
Systems, pp. 3670–3682. ACM, New York, NY, USA, 2016. doi: 10.
1145/2858036.2858387 1, 2, 3, 5
[5]
B. Bach, E. Pietriga, and J.-D. Fekete. Visualizing dynamic networks with
matrix cubes. In Proceedings of the ACM Conference on Human Factors
in Computing Systems, pp. 877–886. ACM, New York, NY, USA, 2014.
doi: 10.1145/2556288.2557010 2
[6]
B. Bach, N. H. Riche, S. Carpendale, and H. Pfister. The emerging genre
of data comics. IEEE Computer Graphics and Applications, 37(3):6–13,
2017. doi: 10.1109/MCG.2017.33 2, 3
[7]
B. Bach, Z. Wang, M. Farinella, D. Murray-Rust, and N. H. Riche. Design
patterns for data comics. In Proceedings of the ACM Conference on
Human Factors in Computing Systems, pp. 1–12. ACM, New York, NY,
USA, 2018. doi: 10.1145/3173574.3173612 2, 3
[8]
A. Bangor, P. Kortum, and J. Miller. Determining what individual sus
scores mean: Adding an adjective rating scale. Journal of usability studies,
4(3):114–123, 2009. 8
[9]
K. Barbieri, O. M. Keshk, and B. M. Pollins. Trading data: Evaluating our
assumptions and coding rules. Conflict Management and Peace Science,
26(5), 2009. 7
[10]
K. Barbieri and O. M. G. Keshk. Correlates of war project trade dataset
codebook, version 4.0, 2016. https://correlatesofwar.org/. 7
[11]
F. Beck, M. Burch, S. Diehl, and D. Weiskopf. A taxonomy and survey of
dynamic graph visualization. Computer Graphics Forum, 36(1):133–159,
2017. doi: 10.1111/cgf.12791 2, 3
[12]
M. Behrisch, B. Bach, N. Henry Riche, T. Schreck, and J.-D. Fekete.
Matrix reordering methods for table and network visualization. Computer
Graphics Forum, 35(3):693–716, 2016. doi: 10.1111/cgf.12935 2
[13]
A. Bezerianos, P. Dragicevic, J. Fekete, J. Bae, and B. Watson. Ge-
neaQuilts: A system for exploring large genealogies. IEEE Transactions
on Visualization and Computer Graphics, 16(6):1073–1081, 2010. doi: 10
.1109/TVCG.2010.159 2
[14]
M. Bostock, V. Ogievetsky, and J. Heer. D
3
: Data-driven documents. IEEE
Transactions on Visualization and Computer Graphics, 17(12):2301–2309,
2011. 4
[15]
J. Brooke et al. Sus: A quick and dirty usability scale. Usability evaluation
in industry, 189(194):4–7, 1996. 8
[16]
M. Burch, M. Hlawatsch, and D. Weiskopf. Visualizing a sequence of a
thousand graphs (or even more). Computer Graphics Forum, 36(3):261–
271, 2017. doi: 10.1111/cgf.13185 2
[17]
K. Caine. Local standards for sample size at chi. In Proceedings of the
ACM Conference on Human Factors in Computing Systems, pp. 981–992.
ACM, New York, NY, USA, 2016. 7
[18]
E. Cakmak, U. Schlegel, D. Jäckle, D. Keim, and T. Schreck. Multiscale
snapshots: Visual analysis of temporal summaries in dynamic graphs.
IEEE Transactions on Visualization and Computer Graphics, 27(2):517–
527, 2020. doi: 10.1109/TVCG.2020.3030398 2, 3
[19]
J. Carifio and R. Perla. Resolving the 50-year debate around using and
misusing likert scales. Medical education, 42(12):1150–1152, 2008. 8
[20]
Q. Chen, S. Cao, J. Wang, and N. Cao. How does automation shape the
process of narrative visualization: A survey on tools. IEEE Transactions
on Visualization and Computer Graphics, 30(8):4429–4448, 2024. doi: 10
.1109/TVCG.2023.3261320 2
[21]
C. Collins, G. Penn, and S. Carpendale. Bubble sets: Revealing set
relations with isocontours over existing visualizations. IEEE Transactions
on Visualization and Computer Graphics, 15(6):1009–1016, 2009. doi: 10
.1109/TVCG.2009.122 7
[22]
A. Dal Col, P. Valdivia, F. Petronetto, F. Dias, C. T. Silva, and L. G. Nonato.
Wavelet-based visual analysis of dynamic networks. IEEE Transactions
on Visualization and Computer Graphics, 24(8):2456–2469, 2017. doi: 10
.1109/TVCG.2017.2746080 2, 3
[23]
C. Dunne and B. Shneiderman. Motif simplification: improving network
visualization readability with fan, connector, and clique glyphs. In Pro-
ceedings of the ACM Conference on Human Factors in Computing Systems,
pp. 3247–3256. ACM, New York, NY, USA, 2013. doi: 10.1145/2470654.
2466444 2
[24]
N. Elmqvist and J. Fekete. Hierarchical aggregation for information visual-
ization: Overview, techniques, and design guidelines. IEEE Transactions
on Visualization and Computer Graphics, 16(3):439–454, 2010. doi: 10.
1109/TVCG.2009.84 2, 3
[25]
V. Filipov, A. Arleo, and S. Miksch. Are we there yet? A roadmap
of network visualization from surveys to task taxonomies. Computer
Graphics Forum, 42(6):e14794, 2023. doi: 10.1111/cgf.14794 2, 3
[26]
X. Gao, B. Xiao, D. Tao, and X. Li. A survey of graph edit distance.
Pattern Analysis and Applications, 13, 2010. 4
[27]
T. Gartner, P. Flach, S. Wrobel, B. Scholkopf, and M. Warmuth. On graph
kernels: Hardness results and efficient alternatives. In Proceedings of
Learning Theory and Kernel Machines, vol. 2777, pp. 129–143, 2003. 4
[28]
S. Ghani, N. H. Riche, and N. Elmqvist. Dynamic insets for context-aware
graph navigation. Computer Graphics Forum, 30(3):861–870, 2011. doi:
10.1111/j.1467-8659.2011.01935.x 2
[29]
S. Hadlak, H. Schumann, C. H. Cap, and T. Wollenberg. Supporting the
visual analysis of dynamic networks by clustering associated temporal
attributes. IEEE Transactions on Visualization and Computer Graphics,
19(12):2267–2276, 2013. doi: 10.1109/TVCG.2013.198 2
[30]
N. Henry and J.-D. Fekete. MatrixExplorer: a dual-representation system
to explore social networks. IEEE Transactions on Visualization and
Computer Graphics, 12(5):677–684, 2006. doi: 10.1109/TVCG.2006.160
2
[31]
N. Henry, J.-D. Fekete, and M. J. McGuffin. NodeTrix: a hybrid visualiza-
tion of social networks. IEEE Transactions on Visualization and Computer
Graphics, 13(6):1302–1309, 2007. doi: 10.1109/TVCG.2007.70582 2
[32]
J. Huang, A. Mishra, B. C. Kwon, and C. Bryan. Conceptexplainer: Inter-
active explanation for deep neural networks from a concept perspective.
IEEE Transactions on Visualization and Computer Graphics, 29(1):831–
841, 2022. doi: 10.1109/TVCG.2022.3209384 8
[33]
J. Hullman, S. M. Drucker, N. H. Riche, B. Lee, D. Fisher, and E. Adar.
A deeper understanding of sequence in narrative visualization. IEEE
Transactions on Visualization and Computer Graphics, 19(12):2406–2415,
2013. doi: 10.1109/TVCG.2013.119 3
[34]
W. Hwang and G. Salvendy. Number of people required for usability
evaluation: the 10±2 rule. Comm. of the ACM, 53(5):130–133, 2010. 7
[35]
P. Isenberg, F. Heimerl, S. Koch, T. Isenberg, P. Xu, C. Stolper, M. Sedl-
mair, J. Chen, T. Möller, and J. Stasko. vispubdata.org: A Metadata Col-
lection about IEEE Visualization (VIS) Publications. IEEE Transactions
on Visualization and Computer Graphics, 23, 2017. doi: 10.1109/TVCG.
2016.2615308 7, 8
[36]
A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford,
D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel,
G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock,
S. Subramanian, S. Yang, S. Antoniak, T. L. Scao, T. Gervet, T. Lavril,
T. Wang, T. Lacroix, and W. E. Sayed. Mixtral of experts. CoRR,
abs/2401.04088, 2024. doi: 10.48550/ARXIV.2401.04088 6
[37]
B. Kale, M. Sun, and M. E. Papka. The state of the art in visualizing
dynamic multivariate networks. Computer Graphics Forum, 42(3):471–
490, 2023. doi: 10.1111/cgf.14856 3
[38]
D. Kang, T. Ho, N. Marquardt, B. Mutlu, and A. Bianchi. ToonNote:
Improving communication in computational notebooks using interactive
data comics. In Proceedings of the ACM Conference on Human Factors
in Computing Systems, pp. 1–14. ACM, New York, NY, USA, 2021. doi:
10.1145/3411764.3445434 2, 3
[39]
H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between
labeled graphs. In Proceedings of the International Conference on Machine
Learning, p. 321–328, 2003. 4
[40]
N. W. Kim, N. H. Riche, B. Bach, G. Xu, M. Brehmer, K. Hinckley,
M. Pahud, H. Xia, M. J. McGuffin, and H. Pfister. DataToon: Drawing
dynamic network comics with pen + touch interaction. In Proceedings of
the ACM Conference on Human Factors in Computing Systems, pp. 1–12.
ACM, New York, NY, USA, 2019. doi: 10.1145/3290605.3300335 2, 3
[41]
P. M. Kogge. Jaccard coefficients as a potential graph benchmark. In
Proceedings of IEEE International Parallel and Distributed Processing
Authorized licensed use limited to: INRIA. Downloaded on December 11,2025 at 13:39:36 UTC from IEEE Xplore. Restrictions apply.
983
kim ET AL.: DG COmiCS: SEmi-AUTOmATiCALLY AUTHORiNG GRAPH COmiCS FOR DYNAmiC GRAPHS
Symposium Workshops, pp. 921–928, 2016. 4
[42]
R. Kosara and J. Mackinlay. Storytelling: The next step for visualization.
IEEE Computer, 46(5):44–50, 2013. doi: 10.1109/MC.2013.36 3
[43]
N. M. Kriege, P.-L. Giscard, and R. C. Wilson. On valid optimal assign-
ment kernels and applications to graph classification. In Advances in
Neural Information Processing Systems, p. 1623–1631, 2016. 4
[44]
B. Lee, C. S. Parr, C. Plaisant, B. B. Bederson, V. D. Veksler, W. D.
Gray, and C. Kotfila. Treeplus: Interactive exploration of networks with
enhanced tree layouts. IEEE Transactions on Visualization and Computer
Graphics, 12(6):1414–1426, 2006. doi: 10.1109/TVCG.2006.106 9
[45]
B. Lee, N. H. Riche, P. Isenberg, and S. Carpendale. More than telling
a story: A closer look at the process of transforming data into visually
shared stories. IEEE Computer Graphics and Applications, 35(5):84–90,
2015. doi: 10.1109/MCG.2015.99 3
[46]
W. Li, S. Schöttler, J. Scott-Brown, Y. Wang, S. Chen, H. Qu, and B. Bach.
Networknarratives: Data tours for visual network exploration and analysis.
In Proceedings of the ACM Conference on Human Factors in Computing
Systems, CHI ’23. Association for Computing Machinery, New York, NY,
USA, 2023. doi: 10.1145/3544548.3581452 2
[47]
G. Ma, N. K. Ahmed, T. L. Willke, and P. S. Yu. Deep graph similarity
learning: a survey. Data Mining and Knowledge Discovery, 35(3):688–
725, 2021. doi: 10.1007/S10618-020-00733-5 4
[48]
P. Mahe and J.-P. Vert. Graph kernels based on tree patterns for molecules.
Machine Learning, 75:3–35, 2009. 4
[49]
T. May, M. Steiger, J. Davey, and J. Kohlhammer. Using signposts for
navigation in large graphs. Computer Graphics Forum, 31(3pt2):985–994,
2012. doi: 10.1111/j.1467-8659.2012.03091.x 2
[50]
S. McCloud. Understanding Comics: The Invisible Art. William Morrow
Paperbacks, 1994. 2, 3, 4, 5
[51]
F. McGee, M. Ghoniem, G. Melançon, B. Otjacques, and B. Pinaud. The
state of the art in multilayer network visualization. Computer Graphics
Forum, 38(6):125–149, 2019. doi: 10.1111/cgf.13610 2, 3
[52]
K. Misue. Area-adaptive drawing of rooted trees. In Proceedings of the
IEEE Pacific Symposium on Visualization, pp. 152–161, 2024. 9
[53]
A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, and
S. Jaiswal. graph2vec: Learning distributed representations of graphs.
CoRR, abs/1707.05005, 2017. 2
[54] J. Nielsen. Usability engineering. Morgan Kaufmann, 1994. 7
[55]
C. Nobre, M. Meyer, M. Streit, and A. Lex. The state of the art in
visualizing multivariate networks. Computer Graphics Forum, 38(3):807–
832, 2019. doi: 10.1111/cgf.13728 2
[56]
G. Palla, A.-L. Barabási, and T. Vicsek. Quantifying social group evolution.
Nature, 446(7136):664–667, 2007. doi: 10.1038/nature05670 7
[57]
C. Plaisant, J. Grosjean, and B. B. Bederson. Spacetree: Supporting explo-
ration in large node link tree, design evolution and empirical evaluation.
In Proceedings of the IEEE Symposium on Information Visualization, pp.
57–64, 2002. doi: 10.1109/INFVIS.2002.1173148 9
[58]
J. Ramon and T. Gartner. Expressivity versus efficiency of graph kernels.
In Proceedings of the First International Workshop on Mining Graphs,
Trees and Sequences, pp. 65–74, 2003. 4
[59]
N. H. Riche, C. Hurter, N. Diakopoulos, and S. Carpendale. Data-Driven
Storytelling. A K Peters/CRC Press, Boca Raton, FL, USA, 2018. 2
[60]
L. E. Rocha, N. Masuda, and P. Holme. Sampling of temporal networks:
Methods and biases. Physical Review E, 96(5):052302, 2017. 9
[61]
G. Rossetti and R. Cazabet. Community discovery in dynamic networks:
a survey. ACM Computing Surveys (CSUR), 51(2):1–37, 2018. 9
[62]
M. Ruži
ˇ
cka. Anwendung mathematisch–statisticher methoden in der
geobotanik (synthetische bearbeitung von aufnahmen), 1958. 4
[63]
B. Saket, P. Simonetto, and S. G. Kobourov. Group-level graph visualiza-
tion taxonomy. CoRR, abs/1403.7421, 2014. doi: 10.48550/arXiv.1403.
7421 2
[64] M. Saraceni. The Language of Comics. Psychology Press, 2003. 4, 5
[65]
E. Segel and J. Heer. Narrative visualization: Telling stories with data.
IEEE Transactions on Visualization and Computer Graphics, 16(6):1139–
1148, 2010. doi: 10.1109/TVCG.2010.179 1, 2, 3
[66]
M. Shin, J. Kim, Y. Han, L. Xie, M. Whitelaw, B. C. Kwon, S. Ko, and
N. Elmqvist. Roslingifier: Semi-automated storytelling for animated
scatterplots. IEEE Transactions on Visualization and Computer Graphics,
29(6):2980–2995, 2023. doi: 10.1109/TVCG.2022.3146329 9
[67]
L. South, D. Saffo, O. Vitek, C. Dunne, and M. A. Borkin. Effective use of
likert scales in visualization evaluations: A systematic review. Computer
Graphics Forum, 41(3):43–55, 2022. doi: 10.1111/cgf.14521 8
[68]
C. D. Stolper, B. Lee, N. H. Riche, and J. Stasko. Emerging and recurring
data-driven storytelling techniques: Analysis of a curated collection of
recent stories. Technical Report MSR-TR-2016-14, Microsoft Research,
2016. 3
[69]
S. Suh, J. Zhao, and E. Law. CodeToon: Story ideation, auto comic gener-
ation, and structure mapping for code-driven storytelling. In Proceedings
of the ACM Symposium on User Interface Software and Technology, pp.
13:1–13:16. ACM, New York, NY, USA, 2022. doi: 10.1145/3526113.
3545617 2, 3
[70]
C. Tong, R. C. Roberts, R. Borgo, S. P. Walton, R. S. Laramee, K. Wegba,
A. Lu, Y. Wang, H. Qu, Q. Luo, and X. Ma. Storytelling and visual-
ization: An extended survey. Information, 9(3):65, 2018. doi: 10.3390/
info9030065 2, 3
[71]
S. van den Elzen, D. Holten, J. Blaas, and J. J. van Wijk. Dynamic network
visualization with extended massive sequence views. IEEE Transactions
on Visualization and Computer Graphics, 20(8):1087–1099, 2014. doi: 10
.1109/TVCG.2013.263 2
[72]
S. van den Elzen, D. Holten, J. Blaas, and J. J. van Wijk. Reducing
snapshots to points: A visual analytics approach to dynamic network
exploration. IEEE Transactions on Visualization and Computer Graphics,
22(1):1–10, 2015. doi: 10.1109/TVCG.2015.2468078 2, 3
[73]
S. Varma, S. Shivam, A. Thumu, A. Bhushanam, and D. Sarkar. Jaccard
based similarity index in graphs: A multi-hop approach. In Proceedings
of IEEE Delhi Section Conference, pp. 1–4, 2022. 4
[74]
C. Vehlow, F. Beck, and D. Weiskopf. Visualizing group structures in
graphs: A survey. Computer Graphics Forum, 36(6):201–225, 2017. doi:
10.1111/cgf.12872 2, 3
[75]
T. Von Landesberger, A. Kuijper, T. Schreck, J. Kohlhammer, J. J. van
Wijk, J.-D. Fekete, and D. W. Fellner. Visual analysis of large graphs:
state-of-the-art and future research challenges. Computer Graphics Forum,
30(6):1719–1749, 2011. doi: 10.1111/j.1467-8659.2011 .01898.x 2, 3
[76]
Q. Wang, Z. Li, S. Fu, W. Cui, and H. Qu. Narvis: Authoring narrative
slideshows for introducing data visualization designs. IEEE Transactions
on Visualization and Computer Graphics, 25(1):779–788, 2019. doi: 10.
1109/TVCG.2018.2865232 2
[77]
Z. Wang, J. Ritchie, J. Zhou, F. Chevalier, and B. Bach. Data comics for
reporting controlled user studies in human-computer interaction. IEEE
Transactions on Visualization and Computer Graphics, 27(2):967–977,
2021. doi: 10.1109/TVCG.2020.3030433 2
[78]
Z. Wang, H. Romat, F. Chevalier, N. H. Riche, D. Murray-Rust, and
B. Bach. Interactive data comics. IEEE Transactions on Visualization
and Computer Graphics, 28(1):944–954, 2022. doi: 10.1109/TVCG.2021.
3114849 2, 3
[79]
Z. Wang, S. Wang, M. Farinella, D. Murray-Rust, N. H. Riche, and
B. Bach. Comparing effectiveness and engagement of data comics and
infographics. In Proceedings of the ACM Conference on Human Factors
in Computing Systems, pp. 1–12. ACM, New York, NY, USA, 2019. doi:
10.1145/3290605.3300483 2
[80]
F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, S. Pan, and H. Liu. Graph learning:
A survey. IEEE Transactions on Artificial Intelligence, 2(2):109–127,
2021. doi: 10.1109/TAI.2021.3076021 2
[81]
J. Xu, Y. Tao, Y. Yan, and H. Lin. Exploring evolution of dynamic networks
via diachronic node embeddings. IEEE Transactions on Visualization and
Computer Graphics, 26(7):2387–2402, 2018. doi: 10.1109/TVCG.2018.
2887230 2, 3
[82]
J. Yan, X.-C. Yin, W. Lin, C. Deng, H. Zha, and X. Yang. A short survey
of recent advances in graph matching. In Proceedings of the International
Conference on Multimedia Retrieval, p. 167–174, 2016. 4
[83]
V. Yoghourdjian, T. Dwyer, K. Klein, K. Marriott, and M. Wybrow. Graph
thumbnails: Identifying and comparing multiple graphs at a glance. IEEE
Transactions on Visualization and Computer Graphics, 24(12):3081–3095,
2018. doi: 10.1109/TVCG.2018.2790961 2
[84]
J. Zhao, S. Xu, S. K. Chandrasegaran, C. Bryan, F. Du, A. Mishra, X. Qian,
Y. Li, and K. Ma. ChartStory: Automated partitioning, layout, and cap-
tioning of charts into comic-style narratives. IEEE Transactions on Vi-
sualization and Computer Graphics, 29(2):1384–1399, 2023. doi: 10.
1109/TVCG.2021.3114211 3, 8
[85]
Z. Zhao, W. Benjamin, N. Elmqvist, and K. Ramani. Sketcholution: Inter-
action histories for sketching. International Journal of Human-Computer
Studies, 82:11–20, 2015. doi: 10.1016/j.ijhcs.2015.04.003 3
[86]
Z. Zhao, R. Marr, and N. Elmqvist. Data comics: Sequential art for data-
driven storytelling. Technical Report 15, Human-Computer Interaction
Laboratory, University of Maryland, College Park, 2015. 1, 2, 3
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